# 观察一个完整的例子 https://github.com/pytorch/examples/blob/master/mnist/main.py

import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from myNet import *
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms
import time
from torch.utils.tensorboard import SummaryWriter

# class Net(nn.Module):
#     def __init__(self):
#         super(Net, self).__init__()
#         self.conv1 = nn.Conv2d(1, 32, 3, 1)
#         self.conv2 = nn.Conv2d(32, 64, 3, 1)
#         self.dropout1 = nn.Dropout2d(0.25)
#         self.dropout2 = nn.Dropout2d(0.5)
#         self.fc1 = nn.Linear(9216, 128)
#         self.fc2 = nn.Linear(128, 10)

#     def forward(self, x):
#         x = self.conv1(x)
#         x = F.relu(x)
#         x = self.conv2(x)
#         x = F.relu(x)
#         x = F.max_pool2d(x, 2)
#         x = self.dropout1(x)
#         x = torch.flatten(x, 1)
#         x = self.fc1(x)
#         x = F.relu(x)
#         x = self.dropout2(x)
#         x = self.fc2(x)
#         output = F.log_softmax(x, dim=1)
#         return output

def train(args, model, device, train_loader, optimizer, epoch):
    model.train()#设置模型处于训练状态，并不是启动训练，启用 BatchNormalization 和 Dropout
    writer = SummaryWriter('runs/experiment_4')
    running_loss=0
    for batch_idx, (data, target) in enumerate(train_loader):#batch_idx是序号
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()#梯度设置为0
        output = model(data)#求得输出
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        running_loss+=loss.item()
        if batch_idx % args.batch_size == (args.batch_size-1):    # every args.batch_size mini-batches...
            # ...log the running loss
            writer.add_scalar('training loss',
                            running_loss / args.batch_size,
                            epoch * len(train_loader) + batch_idx)
            running_loss = 0.0
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
            if args.dry_run:
                break
    print('Finished Training')


def test(model, device, test_loader):
    model.eval()#不启用 BatchNormalization 和 Dropout
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')#创建一个ArgumentParser对象
    parser.add_argument('--batch-size', type=int, default=72, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=5, metavar='N',
                        help='number of epochs to train (default: 3)')
    parser.add_argument('--lr', type=float, default=0.0007, metavar='LR',
                        help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.9, metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--dry-run', action='store_true', default=False,
                        help='quickly check a single pass')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--save-model', action='store_true', default=True,#使能保存模型
                        help='For Saving the current Model')
    # 以下语句有所变更，由于jupyter不能从命令行接收参数，改为空参数，即全部使用默认值。
    args = parser.parse_args(args=[])
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    kwargs = {'batch_size': args.batch_size}
    if use_cuda:
        kwargs.update({'num_workers': 1,
                       'pin_memory': True,
                       'shuffle': True},
                     )

    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
        ])
    dataset1 = datasets.MNIST('./data', train=True, download=True,
                       transform=transform)
    dataset2 = datasets.MNIST('./data', train=False,
                       transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
    # net=Net()#画出网络图
    # image = torch.randn(1,1,28,28)
    # writer.add_graph(net, image)
    # writer.flush()
    # writer.close()
    net = Net()
    model = net.to(device)#实例化网络，说明在哪个device上训练
    optimizer = optim.Adam(model.parameters(), lr=args.lr)#优化器的选择

    scheduler = StepLR(optimizer, step_size=2, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)#训练模型参数
        test(model, device, test_loader)#检验模型准确率
        print("第%d个epoch的学习率：%f" % (epoch, optimizer.param_groups[0]['lr']))
        scheduler.step()

    if args.save_model:
        torch.save(model.state_dict(), "mnist_cnn.pt")

t_start = time.time()
main()
print(f'escape time: {time.time() - t_start} seconds')